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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits
Christina B. Azodi, Emily Bolger, Andrew McCarren, et al.
G3 Genes Genomes Genetics (2019) Vol. 9, Iss. 11, pp. 3691-3702
Open Access | Times Cited: 162

Showing 1-25 of 162 citing articles:

Machine learning in plant science and plant breeding
Aalt D. J. van Dijk, Gert Kootstra, Willem Kruijer, et al.
iScience (2020) Vol. 24, Iss. 1, pp. 101890-101890
Open Access | Times Cited: 213
A review of deep learning applications for genomic selection
Osval A. Montesinos‐López, Abelardo Montesinos‐López, Paulino Pérez‐Rodríguez, et al.
BMC Genomics (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 207
LightGBM: accelerated genomically designed crop breeding through ensemble learning
Jun Yan, Yuetong Xu, Qian Cheng, et al.
Genome biology (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 180
Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction
Yunbi Xu, Xingping Zhang, Huihui Li, et al.
Molecular Plant (2022) Vol. 15, Iss. 11, pp. 1664-1695
Open Access | Times Cited: 151
Machine learning bridges omics sciences and plant breeding
Jun Yan, Xiangfeng Wang
Trends in Plant Science (2022) Vol. 28, Iss. 2, pp. 199-210
Closed Access | Times Cited: 74
Genomic selection in plant breeding: Key factors shaping two decades of progress
Admas Alemu, Johanna Åstrand, Osval A. Montesinos‐López, et al.
Molecular Plant (2024) Vol. 17, Iss. 4, pp. 552-578
Open Access | Times Cited: 64
A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction
Wei Zhou, Zhengxiao Yan, Liting Zhang
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 28
Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species
Laura M. Zingaretti, Salvador A. Gezan, Luís Felipe V. Ferrão, et al.
Frontiers in Plant Science (2020) Vol. 11
Open Access | Times Cited: 117
Harnessing Crop Wild Diversity for Climate Change Adaptation
Andrés J. Cortés, Felipe López-Hernández
Genes (2021) Vol. 12, Iss. 5, pp. 783-783
Open Access | Times Cited: 96
Genomic Prediction of Agronomic Traits in Common Bean (Phaseolus vulgaris L.) Under Environmental Stress
Beat Keller, Daniel Ariza-Suárez, Juan De La Hoz, et al.
Frontiers in Plant Science (2020) Vol. 11
Open Access | Times Cited: 73
Open problems in human trait genetics
Nadav Brandes, Omer Weissbrod, Michal Linial
Genome biology (2022) Vol. 23, Iss. 1
Open Access | Times Cited: 63
Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics
Jacob I. Marsh, Haifei Hu, Mitchell Gill, et al.
Theoretical and Applied Genetics (2021) Vol. 134, Iss. 6, pp. 1677-1690
Closed Access | Times Cited: 61
Plant Genotype to Phenotype Prediction Using Machine Learning
Monica F. Danilevicz, Mitchell Gill, Robyn Anderson, et al.
Frontiers in Genetics (2022) Vol. 13
Open Access | Times Cited: 56
Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction
Mitchell Gill, Robyn Anderson, Haifei Hu, et al.
BMC Plant Biology (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 38
Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine
Imran Zafar, Shakila Anwar, Faheem kanwal, et al.
Biomedical Signal Processing and Control (2023) Vol. 86, pp. 105263-105263
Closed Access | Times Cited: 32
Harness the power of genomic selection and the potential of germplasm in crop breeding for global food security in the era with rapid climate change
Tianhua He, Chengdao Li
The Crop Journal (2020) Vol. 8, Iss. 5, pp. 688-700
Open Access | Times Cited: 68
Using machine learning to improve the accuracy of genomic prediction of reproduction traits in pigs
Xue Wang, Shaolei Shi, Guijiang Wang, et al.
Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 35
Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics‐assisted breeding
Javaid Akhter Bhat, Xianzhong Feng, Zahoor Ahmad Mir, et al.
Physiologia Plantarum (2023) Vol. 175, Iss. 4
Closed Access | Times Cited: 17
Using Local Convolutional Neural Networks for Genomic Prediction
Torsten Pook, Jan A. Freudenthal, Arthur Korte, et al.
Frontiers in Genetics (2020) Vol. 11
Open Access | Times Cited: 46
GMStool: GWAS-based marker selection tool for genomic prediction from genomic data
Seongmun Jeong, Jae‐Yoon Kim, Namshin Kim
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 40
Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs
Lance F. Merrick, Arron H. Carter
The Plant Genome (2021) Vol. 14, Iss. 3
Open Access | Times Cited: 38
Genomic Prediction: Progress and Perspectives for Rice Improvement
Jérôme Bartholomé, Parthiban Thathapalli Prakash, Joshua N. Cobb
Methods in molecular biology (2022), pp. 569-617
Closed Access | Times Cited: 23
A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species
Maura John, Florian Haselbeck, Rupashree Dass, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 22
Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence
Stella Dimitsaki, George Gavriilidis, Vlasios K. Dimitriadis, et al.
Artificial Intelligence in Medicine (2023) Vol. 137, pp. 102490-102490
Open Access | Times Cited: 15
Advancing artificial intelligence to help feed the world
Ben J. Hayes, Chensong Chen, Owen Powell, et al.
Nature Biotechnology (2023) Vol. 41, Iss. 9, pp. 1188-1189
Closed Access | Times Cited: 13
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